Mobile communications devices have become an integral part of society over the last two decades. The typical mobile communications device includes an antenna, and a transceiver coupled to the antenna. The transceiver and the antenna cooperate to transmit and receive communications signals.
Before transmission, the typical mobile communications device modulates digital data onto an analog signal. As will be readily appreciated by the skilled person, there is a plurality of modulations available for most applications. Some particularly advantageous modulations include, for example, continuous phase modulation (CPM). The constant envelope characteristics of this modulation provide for lower energy demands on the power amplifier of mobile communications devices, for example, by reducing the peak-to-average power ratio (PAPR), increasing average transmit power (providing greater transmission range), and increasing amplifier efficiency, i.e. allowing the use of non-linear amplifiers such as Class C amplifiers. Moreover, CPM provides for efficient use of available bandwidth.
A potential drawback of CPM modulations is the use of the inherent memory of the modulation when demodulating/decoding the waveform in order to obtain good demodulator performance. When the mobile communications device receives a transmitted signal that uses a modulation with memory, the decoder uses not only the current signal portion to demodulate but in addition uses information from previous signal portions, i.e. memory, to demodulate the current signal. In other words, the phase of the transmitted signal is dependent on previous signaling intervals.
Decoding modulations with memory increases the computational and memory demands on the transceiver, i.e. a maximum likelihood sequence estimator (MLSE), a hard decision device, or the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm (also known as the Maximum a posteriori probability (MAP) decoder), a soft decision device, are typically used to demodulate modulations with memory, thereby increasing the complexity of the device, which may be undesirable in a limited power compact mobile device. More so, when the received signal has a multipath component to it, the size of the MLSE or MAP trellis structure used to demodulate the signal grows exponentially, which may make practical implementation in a mobile communications device difficult since computational resources are limited.
When bandwidth efficient modulations, such as CPM, are concatenated with outer convolutional forward error correction (FEC) codes, the demodulation and multipath equalization processes may require a large amount of computational resources. In particular, the combined CPM/multipath MAP trellis structure may become very large and onerous in computational overhead. More so, the branch metric computations must be re-computed for every state, for every branch, and for every iteration in iterative applications.
In the typical mobile communications device where multipath is present, the MAP branch metrics for the combined CPM/MULTIPATH trellis structure is computed on-the-fly and based upon the following formula.
      b          t              s        ,        k              =            Ext              t                  s          ,          k                      +                  ∑                  l          =          0                          L          -          1                    ⁢                        (                                    y                              t                l                                      -                                          ∑                                  i                  =                  0                                                  N                  -                  1                                            ⁢                                                h                                      est                    i                                                  ⁢                                  w                                      t                                          s                      ,                      k                      ,                      l                                                                                                    )                2            Where bts,k is the branch metric at symbol time t for state s and data Extts,k is the extrinsic information for symbol time t, state s, and data k, ytl is the received sample 1 at time t, hesti is tap i of the channel estimate, L is the number of samples per symbol, N is the total number of taps in the channel estimate (which is equal to L samples per symbol times M symbols of multipath), and wts,k,l are the CPM samples (past and current) for symbol time t, state s, sample 1, using either the symbol memory of the current MAP trellis state or the survivor path memory of the current state and the data k. Of course, using this approach, the computational resources of the typical mobile communications device may be taxed since these operations may include a large number of complex multiplications.
One approach is disclosed in U.S. Patent Application Publication No. 2003/0118093 to Bohnhoff et al., which discloses a Viterbi equalizer (a hard decision device) for receiving a signal subject to interference. The equalizer performs operations on each channel state and calculates in advance metric increments relating to all the transitions from a state that can be predetermined in the time step k to the states that can be reached by the transitions in the time step k+1.